Outlier-detection.R

####                     Outlier detection          ######

x<-c(10,14,12,15,23,13,19,21,16,17,25) # x is  a vector of length 11
length(x)

## [1] 11

y<-c(10,14,12,15,23,13,19,21,16,17,42)
z<-c(13,14,12,15,23,21,28,31,32,69,73)


quantile(x,.25)#Q1

##  25% 
## 13.5

quantile(x,.50)#Q2

## 50% 
##  16

quantile(x,.75)#Q3

## 75% 
##  20

IQR(x)# IQR of x

## [1] 6.5

####               Function for detecting outlier    #######  

Outlier<-function(x){
  lf<-quantile(x,.25)-1.5*IQR(x)
  uf<-quantile(x,.75)+1.5*IQR(x)
  outlier_index<-x<lf|x>uf
  x_outliers<-x[outlier_index]
  
  if(length(x_outliers)==1){
    cat("The outlier is :",x_outliers)
    }else if(length(x_outliers)>1){
    cat("The outliers are :",x_outliers)
    }else{
    cat("There is no outlier")
    }
  par(mfrow=c(1,2))  
  boxplot(x,main="Boxplot",col="steelblue")
  hist(x,main = "Histogram",col="grey")
}

Outlier(x)

## There is no outlier

Outlier(y)

## The outlier is : 42

Outlier(z)

## The outliers are : 69 73